Abstract
A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staphylococcu aureus, Escherichia coli and Pseudomonas aeruginosa) in this experiment. In addition, KELM is compared with five existing classification methods: Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), extreme learning machine (ELM), k-nearest neighbor (KNN) and support vector machine (SVM). Meanwhile, three traditional optimization methods including particle swarm optimization algorithm (PSO), genetic algorithm (GA) and grid search algorithm (GS) and four kernel functions (Gaussian kernel, linear kernel, polynomial kernel and wavelet kernel) for KELM are discussed in this experiment. Finally, the QPSO-KELM model is also used to deal with another two experimental E-nose datasets in the previous experiments. The experimental results demonstrate the superiority of QPSO-KELM in various E-nose applications.
Highlights
An electronic nose (E-nose), combined with artificial intelligence algorithms, is designed for mimicking the mammalian olfactory system to recognize gases and odors
Component and first order harmonic component), and approximation coefficients of db1 wavelet of of component and first order harmonic component), and approximation coefficients of db1 wavelet sensor response curve are chosen to be on behalf of the characteristics of E-nose signals from two sensor response curve are chosen to be on behalf of the characteristics of E-nose signals from two transform domains [47,48,49,50]
A new methodology based on the quantum-behaved particle swarm optimization (QPSO)-Kernel Extreme Learning Machine (KELM) model has been presented to enhance the performance of an E-nose for wound infection detection
Summary
An electronic nose (E-nose), combined with artificial intelligence algorithms, is designed for mimicking the mammalian olfactory system to recognize gases and odors. E-nose comprises several non-specific sensors and will generate characteristic patterns when exposed to odorant materials. Patterns of known odorants can be used to construct a database and train a pattern recognition model through quite a few pattern recognition algorithms. In this way, something unknown which can be discriminated by its odor is classified well [1,2,3]. Much work has been done to investigate the E-nose technology which has been widely used in a multitude of fields, such as food quality control [4,5,6,7], disease diagnosis [8,9,10,11], environment quality assessment [12,13] and agriculture [14,15,16]. In the pattern recognition training data are employed to train the classifier
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